Integrated controlling system

ABSTRACT

An integrated control for a subject such as an engine installed in a vehicle or vessel is conducted by the steps of: determining the characteristics of a user and/or using conditions; and changing characteristics of a control system of a subject in accordance with the determined characteristics. Normally, the control system includes: a reflection hierarchy for outputting a basic output; a learning hierarchy for learning and operation; and an evolutionary-adaptation hierarchy for selecting the most adaptable module. The subject is &#34;trained&#34; to suit the characteristics of the user and/or the using conditions.

This is a continuation application of U.S. patent application Ser. No.08/884,210, filed Jun. 27, 1997, now U.S. Pat. No. 6,021,369 issued Feb.1, 2000.

BACKGROUND OF THE INVENTION

This invention relates to an integrated controlling system, andparticularly to that for comprehensively controlling a subject.

Heretofore, when a control system or control characteristics of asubject, such as vehicles and electrical appliances, is designed,imaginary users are selected, and the users' preferences and their usingconditions are taken into consideration. The characteristics of thesubject are determined in such a way as to adapt the subject to users inas broad a range as possible.

However, each individual user has a particular and unique personality,and thus, their preferences are diverse. Thus, there is a problem inthat even if imaginary users are selected to develop and design aproduct for the users by presuming the users' preference, it isimpossible to satisfy all of the users of the product.

In order to solve the above problem, prior to purchase of a product, aprospective user is requested to determine whether or not the product issatisfactory to the user after checking the characteristics of theproduct in light of the user's preferences. However, it is troublesomefor the user to check the characteristics of the product before thepurchase. Further, because a series of products are often operated orcontrolled by characteristics common in the products, although thedesign of the product is changed depending on the user's preferences,the user may not like other operational characteristics. Thus, althoughthe design is appealing to some prospective users, the users may notpurchase the product since the operational characteristics do not appealto them. In the other words, there is another problem in that the rangeof users is limited and depends on the operational characteristics.

An objective of the present invention is to provide a integrated controlsystem to construct characteristics which can satisfy all users.

SUMMARY OF THE INVENTION

One important aspect of the present invention attaining the aboveobjective is an integrated control method comprising the steps of:determining the characteristics of a user and/or using conditions; andchanging characteristics of a control system of a subject in accordancewith the determined characteristics. In the above, preferably, saidcontrol system outputs a basic output to control the subject, and thechanging step comprises the steps of: creating multiple control modulesfor representing at least one factor to be controlled; selecting atleast one control module most adaptable for a current operational statebased on the determined characteristics of the user and/or usingconditions; learning information from said at least one control module;compensating for the basic output based on the result of the selectionand the learning; and controlling the subject using the outputcompensated for.

According to the present invention, the subject is "trained" to suit thecharacteristics of the user and/or the using conditions, thereby easingcontrol of the subject particularly for the user and enjoying trainingand adapting the subject to the user's preference.

In the above, preferably, said control system comprises: a reflectionhierarchy for outputting the basic output reflectively in response toinput from the using conditions; an evolutionary-adaptation hierarchyfor conducting the creating step, the selecting step, and thecompensating step; and a learning hierarchy for conducting the learningstep and the compensating step. In the above, preferably, said learninghierarchy comprises a control system for learning and a control systemfor operation, both control systems being interchangeable, wherein whilethe control system for learning is learning, the control system foroperation is controlling the subject in cooperation with the reflectionhierarchy.

In the above, preferably, the evolutionary-adaptation hierarchy isinactivated when the control system for learning completes learning.Further, after being inactivated, the evolutionary-adaptation hierarchyis activated at given intervals to check drift between an actual stateand a state controlled by the reflection hierarchy and the controlsystem for operation in the learning hierarchy, and when there is drift,the evolutionary-adaptation hierarchy resumes the creating step and theselecting step. Accordingly, by checking the control particulars atgiven intervals, it is possible to constantly maintain the most suitableoperation against a change in the using environment or deteriorationwith age.

Further, in the above method, parameter-obtaining devices are not newlyrequired. Existing devices can be used for obtaining necessaryparameters, thereby lowering the cost.

When the subject to be controlled is an engine installed in a vehicle,the operation characteristics of the engine can be changed to suit thedriver's preferences, and when control is conducted based on thedriver's skill, suitable driving performance can be realized inaccordance with the driver's skill and its improvement.

Since the user can train the subject (engine) based on the user'spreference after its purchase, the user can give less weight to thecharacteristics of the engine itself, and can select a vehicle from awide range at purchase.

When the subject to be controlled is an auxiliary power unit installedin a bicycle or a wheelchair, the characteristics of the auxiliary powerunit (motor) can be changed to suit the user's preferences, therebyeffecting assistance most customized to each individual user.

When the subject to be controlled is a robot, the characteristics of therobot can be changed to suit the user's preferences, thereby operatingthe robot in a way most suitable to each individual user.

When the subject to be controlled is a suspension system or seat, thecharacteristics of the damper of the suspension system or seat can bechanged to suit the user's preferences, thereby obtainingcharacteristics of the damper most suitable to each individual user.

When the subject to be controlled is a steering system of a vehicle, thecharacteristics of steering control can be changed to suit the user'spreferences, thereby obtaining customized steering controlcharacteristics most suitable to each user.

The present invention can be applied not only to a method but also to asystem. An appropriate system can be constructed accordingly. Inaddition, although the present invention can advantageously andpreferably be applied to an engine, it can be applied to other machinesas described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the basic principle of an integratedcontrol system according to the present invention.

FIG. 2 is a flow chart of an integrated control system on a time basis,according to the present invention.

FIG. 3 is a schematic view showing the relationship between an engineand a control device performing the integrated control system of thepresent invention.

FIG. 4 is a schematic block diagram of a control unit used in an enginecontrol system according to the present invention.

FIG. 5 is a flow chart showing a basic behavior of theevolutionary-adaptation hierarchy according to the present invention.

FIG. 6 is a distribution pattern of the maximum r.p.m. at each gearposition within a certain time period, which is used in the evaluationsystem of the evolutionary-adaptation hierarchy. FIG. 6a shows a patternwhen the r.p.m. is increased in low gears, and FIG. 6b shows a patternwhen the gear is shifted up to high gears at an early stage.

FIG. 7 is a distribution pattern of the maximum r.p.m. at each gearposition within a certain time period, which is used in the evaluationsystem of the evolutionary-adaptation hierarchy. FIG. 7a shows a patternwhen each gear is used (normal driving), FIG. 7b shows a pattern whenonly the first, second, and third gears are used the r.p.m. is increasedin low gears (congested road), and FIG. 7b shows a pattern when only thefifth and sixth gears are used (freeway).

FIG. 8 shows a neural network which learns the relationship between themaximum r.p.m. at each gear position and a driving state index P.

FIG. 9 shows the relationship between the acceleration-weighting ratio αand the driving state index P, used in the evolutionary-adaptationhierarchy according to the present invention.

FIG. 10 shows a hierarchical neural network comprising two inputs andone output, which network is installed in the evolution system of theevolutionary-adaptation hierarchy.

FIG. 11 is a flow chart showing the evolution of the fuel efficiencymodule using genetic algorithm according to the present invention.

FIG. 12 is a schematic diagram showing an embodiment wherein a firstgeneration is created, which is composed of multiple individuals An(n)(n=1-9) encoded by coupling coefficients, used as genes, of the neuralnetwork constructing the fuel efficiency module.

FIG. 13 is a diagram showing the relationship between a change in thespeed of the vehicle and an acceleration evaluation index when thedegree of the throttle opening is constant.

FIG. 14 is a diagram showing the alternate evolutionary processes in thefuel efficiency module and the acceleration module in theevolutionary-adaptation hierarchy.

FIG. 15 is a diagram showing how to obtain input and output data tocontrol the engine, which are the sum of actual data of operation, anevolutionary compensation, and a basic compensation.

FIG. 16 is a diagram showing how to renew a set of educator data,wherein old educator data, whose Euclidean distance from the new data ina set of educator data is less than a given value, are replaced with thenew data.

FIG. 17 is a diagram showing how to presume the driving state index Pusing an IF-THEN rule, "if the maximum engine speed is high in the firstgear and in the second gear, the driving state index P is extremelyhigh" according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The integrated control system of the present invention will be explainedfurther with reference to an embodiment shown in the figures describedbelow.

Outline of Integrated Control System

FIG. 1 is a block diagram showing the basic principle of an integratedcontrol system according to the present invention. As shown in FIG. 1,the integrated control system of this embodiment has three hierarchies,i.e., a reflection hierarchy, a learning hierarchy, and an evolutionaryhierarchy, into which information related to the subject to becontrolled, such as that related to a dynamic state, is input. Based onthe information, a control base value is determined in the reflectionhierarchy, and compensations for the control base value are determinedin the learning hierarchy and in the evolutionary-adaptation hierarchy.A final control output is determined based on the control base value andthe compensations.

The functions of the reflection hierarchy, the learning hierarchy, andthe evolutionary-adaptation hierarchy in the integrated control systemwill be explained.

The reflection hierarchy is a hierarchy installing a relationshipbetween information related to the subject to be controlled (hereinafterreferred to as external information) and a control base value for theexternal information, in a control system such as in the form ofequations, maps, fuzzy rules, neural network, or subsumptionarchitecture. When the external information is input thereinto, acontrol base value is determined for the external information input fromthe control system. The above subsumption architecture is known to be abehavioral artificial intelligence which conducts parallel processing.

The evolutionary-adaptation hierarchy is comprised of an evaluationsystem and an evolutionary-adaptation system. The evaluation system isto input the external information and/or information related to user'scharacteristics (for example, preference, skill, or a state at themoment), and/or information related to user's using conditions (forexample, a change in using environment), and based on the aboveinformation such as the external information, the characteristics of theuser and/or the using conditions are evaluated. Theevolutionary-adaptation system is provided with at least one controlmodule to compensate for the control base value to suit thecharacteristics of the user and/or the using conditions. The at leastone control module genetically evolves based on the determination in theevaluation system, and converts into a control module most suitable atthe moment. Upon obtaining the most suitable control module, the controlmodule is fixed in the evolutionary system which then outputs anevolutionary compensation which compensates for the control base valueoutput from the reflection hierarchy.

The learning hierarchy is comprised of two control systems mutuallyswitchable, one for learning and the other for operation. While thecontrol system for operation operates control, the control system forlearning learns the relationship of input and output regarding the mostsuitably evolved control module in the evolutionary-adaptation hierarchyin combination with the relationship of input and output regarding thecontrol system for operation in the learning hierarchy. After completingthe learning in the control system for learning, the control systemoperating control and the control system that has finished learning areswitched over, whereby the control system after learning startsoperating control using a control module obtained from the learning,whereas the control system previously operating control startsfunctioning as a control system for learning. Incidentally, the controlsystem in the learning hierarchy is set so as to output zero at thebeginning, i.e., control is conducted by the reflection hierarchy andthe evolutionary hierarchy at the beginning.

The evolutionary hierarchy returns the output to zero after causing thelearning hierarchy to learn information related to the most suitablecontrol module. The evolutionary hierarchy functions at given intervalsto evaluate the user's preference and/or the using environment and tocause the control module to evolve. If the evaluation in the case ofadding the output from the evolutionary hierarchy is better than in thecase of no addition of the output from the evolutionary hierarchy, theevolutionary hierarchy causes again the learning hierarchy to learninformation related to the most suitable control module.

In addition, the information related to the after-learningcontrol-module in the learning hierarchy is readably saved in externalmemory means such as an IC card and a floppy disk. The user can pull outthe information related to the most suitable control module in the pastfrom the external memory means, according to the user's need, and theuser can thereby output a basic compensation from the learning hierarchybased on the information. In the above, when the user pulls out theinformation related to the most suitable control module in the past fromthe external memory means, and operates the learning hierarchy, whilethe learning hierarchy is functioning by the pulled-out control module,the output of the evolutionary hierarchy is set at zero, i.e.,evolutionary processes of the control module(s) are stopped.

According to the integrated control system described above, by thefunction of each hierarchy, the control output is changing in accordancewith changes in the characteristics such as the user's preference andthe using environment, and as a result, the characteristics of thesubject to be controlled are changing in accordance with thecharacteristics of the user and/or the using conditions. In the presentinvention, the state, wherein the characteristics of the subject evolvesto suit them to the characteristics of the user and/or the usingconditions, is referred to as "training".

Control Flow of Integrated Control System

FIG. 2 is a flow chart of the integrated control system on a time basis.

In an initial state, the output from the learning hierarchy is zero(step a), and thus, immediately after the subject is activated, thesubject is controlled only by the control base value from the reflectionhierarchy.

After the subject is activated, the evolutionary-adaptation hierarchyevaluates the characteristics of the user and/or the using conditions,and in accordance with the evaluated value, the evolutionary-adaptationhierarchy causes a control module(s) to evolve (step b).

By genetically evolving each control module, the evolutionary-adaptationhierarchy obtains at least one control module most preferable at themoment (step c).

The evolutionary-adaptation hierarchy fixes the control module(s) to themost preferable control module obtained in step c, and outputs anevolutionary compensation using the fixed control module, therebycompensating for the control base value output from the reflectionhierarchy. The learning hierarchy learns, in the control system forlearning, the relationship of input and output in theevolutionary-adaptation hierarchy wherein the control module(s) is fixedto the most preferable control module, in combination with therelationship of input and output in the control system for operation inthe learning hierarchy. In the above, whereas the output from thecontrol system for operation in the learning hierarchy is zero in theinitial state, after learning, the basic compensation from the learninghierarchy and the evolutionary compensation from theevolutionary-adaptation hierarchy compensate for the control base valuefrom the reflection hierarchy (step d).

When the difference between the value, which is obtained by adding theoutput from the control system for learning in the learning hierarchy tothe control base value, and the value, which is the actual output(control base value+basic compensation+evolutionary compensation), issmaller than a predetermined threshold, the learning in the learninghierarchy is completed. The control system for learning and the controlsystem for operation are then switched over, i.e., the previous controlsystem for learning functions as a control system for operation whilethe previous control system for operation functions as a control systemfor learning (step e). In this way, control is conducted by thereflection hierarchy and the learning hierarchy (step f).

After the evolutionary-adaptation hierarchy causes the learninghierarchy to learn information related to the most preferable controlmodule, the evolutionary-adaptation hierarchy is activated at givenintervals to evaluate a drift by age in the control particulars of thelearning hierarchy (step g). In detail, if the maximum adaptability isno longer improved in the initial generation when the control module(s)of the evolutionary-adaptation hierarchy is genetically evolved, it isdetermined that there is no drift in the control particulars of thelearning hierarchy, and then step f is activated wherein control by thereflection hierarchy and the learning hierarchy is continued;conversely, if the maximum adaptability is further improved, it isdetermined that a drift is detected, and then step b is activatedwherein a new control module most adaptable in theevolutionary-adaptation hierarchy.

Integrated Control System Adapted to Engine of Vehicle

In an embodiment, the subject to be controlled is an engine installed invehicles or vessels. However, in the integrated control system of thepresent invention, no restriction is imposed on the type of machine tobe controlled, and as with the engine, a robot used in machine tools, amotor used in electrically-driven vehicles, or the like can becontrolled by adopting the integrated control system based on the sameprinciple as in the engine.

FIG. 3 is a schematic view showing the relationship between an engine 1and a control device 10 performing the above-described integratedcontrol system.

As shown in FIG. 3, the control device 10 controls the engine in such away that fuel efficiency and acceleration performance are compatiblewith each other, based on information input into the control device,such as the engine speed (r.p.m.), the intake-negative pressure, thedegree of the throttle opening (angle), the changing rate of thethrottle opening, the atmospheric pressure, the intake temperature, thetemperature of cooling water, and the position of the gear.

FIG. 4 is a schematic block diagram of the control device 10.

The control device 10 is comprised of the reflection hierarchy, thelearning hierarchy, and the evolutionary-adaptation hierarchy asdescribed above.

Reflection Hierarchy Adapted to Engine Control

The reflection hierarchy receives signals such as those of the enginespeed, the intake-negative pressure, the degree of the throttle opening,the changing rate of the throttle opening, the atmospheric pressure, theintake temperature, and the temperature of cooling water, and thereflection hierarchy determines and outputs a basic value offuel-injection quantity (i.e., the control base value of the fuelinjection device) using an equation formed by modeling numericalformulae obtained from the above input signals.

Evolutionary-adaptation Hierarchy Adapted to Engine Control

The evolutionary-adaptation hierarchy is comprised of an evaluationsystem and an evolutionary-adaptation system.

FIG. 5 is a flow chart showing a basic behavior of theevolutionary-adaptation hierarchy. The basic behavior of theevolutionary-adaptation hierarchy will be explained with reference tothis chart.

The evaluation system is equipped with a neural network (see FIG. 8)which has learned the relationship between a distribution pattern of themaximum r.p.m. at each gear position within a certain time period (seeFIGS. 6 and 7) and a driving state index P. A gear position signal andan engine r.p.m. signal are input into the neural network (step 1) todetermine a driving state index P (step 2). For example, a user wholikes sporty driving tends to increase the engine speed in low gears,and the distribution pattern of the engine speed can be represented byFIG. 6a. Conversely, a user who likes staid driving intend to shift upto high gears at an early stage, and the distribution pattern of theengine speed can be represented by FIG. 6b. When the neural network, asshown in FIG. 8, has learned the relationship between the engine speedand the driving state index P in such a way that driving state index Pis high when the distribution pattern is represented by FIG. 6a, whiledriving state index P is low when the distribution pattern isrepresented by FIG. 6b, the neural network shows that the higher thepreference for sporty driving, the higher the driving state index Pbecomes, while the higher the preference for staid driving, the lowerthe driving state index P becomes. In this way, the driving state indexP can represent the user's preference.

In addition, as shown in FIG. 7a, when all of the gears from the firstthrough the sixth gears are used in a certain time period, the drivingstate index P is high. When only low gears are used in a certain timeperiod, as shown in FIG. 7b, the driving state index P is slightly high.When only high gears are used in a certain time period, the drivingstate index P is low. In a normal vehicle, all of the gears are usedduring in normal driving, only low gears are used when on a congestedroad, and only high gears are used when on a freeway, and thus,according to the driving state index P output from the neural networkwhich has learned as above, FIGS. 7a, 7b, and 7c presume normal driving,driving on a congested road, and driving on a freeway, respectively.

In the evaluation system, based on the driving state index P, thedriving state at the moment is presumed, and an acceleration-weightingratio α is determined by judging whether the user prefers fuelefficiency or acceleration performance (step 3).

The acceleration-weighting ratio α can be determined from apredetermined equation of an acceleration-weighting ratio α and adriving state index P, as shown in FIG. 9. For example, when driving ona freeway and the driving state index P is low (see FIG. 7c), theacceleration-weighting ratio α is low, i.e., fuel efficiency is regardedas important. Conversely, when the driving state index P is high, i.e.,sporty driving (FIG. 6a), the acceleration-weighting ratio α is high,i.e., acceleration performance is regarded as important.

The evolutionary-adaptation system is comprised of at least one fuelefficiency module and one acceleration module, and causes them tomutually cooperate and compete to obtain a change to be more adaptableor suitable. Each module is comprised of a hierarchical neural networkcomprising two inputs and one output, as shown in FIG. 10. The fuelefficiency module aims at improving fuel efficiency, and theacceleration module aims at improving acceleration performance. Theinput into each module comprises the engine speed and the degree of thethrottle opening. Based on the input, each module outputs a compensationfor the fuel-injection quantity (i.e., compensation for the control basevalue from the reflection hierarchy).

The evolutionary-adaptation hierarchy evolves the degree of coupling inthe neural network constructing the fuel efficiency module and that ofthe acceleration module, alternately, using a genetic algorithm inaccordance with the user's preference and the using environment, i.e.,the evaluation by the evaluation system (step 4). After completing theevolution of both modules, the degree of coupling of each module isfixed to the evolved degree, and using an evolutionary compensation Ybased on the output from both modules, the engine is controlled (step5). In the above, the genetic evolution of the modules in step 4 takesplace in each module alternately, wherein while the fuel efficiencymodule is evolving, the degree of coupling of the neural network in theacceleration module is fixed, and vice versa.

The engine control using an evolutionary compensation Y in theevolutionary-adaptation hierarchy continues until the learning in thelearning hierarchy described below is completed (step 6). Uponcompletion of the learning in the learning hierarchy, the fuelefficiency module and the acceleration module are reset, and the outputfrom the evolutionary-adaptation hierarchy is set at zero (step 7).

Evolution of Module Using Genetic Algorithm

The evolution of a module using a genetic algorithm will be explainedwith reference to a flow chart of FIG. 12 showing the evolution of thefuel efficiency module, as an example.

First, as shown in step 1 in FIG. 12, in the fuel efficiency module, afirst generation is created, which is composed of multiple individualsAn(n) (n=1-9, nine individuals in this embodiment) encoded by couplingcoefficients, used as genes, of the neural network constructing the fuelefficiency module. Initial values of the genes, i.e., the couplingcoefficients, of each individual are randomly set in a predeterminedrange (e.g., approximately -10 to 10). In the above, by creating oneindividual having a gene value (coupling coefficient value) of zero, itis possible to avoid abating, in the process of evolution, theperformance characteristics lower than those before evolution.

For one of the individuals created in step 1, individual An(1) forexample, output x of the neural network is determined using the fuelefficiency module based on the actual information (the engine speed andthe degree of the throttle opening) (step 4). Output yf of the fuelefficiency module is determined by linear transformation of the output xusing equation (1) (step 5). In the above, the information is the enginespeed and the degree of the throttle opening, which are normalized.

    yf=2*Gx-G                                                  (1)

wherein yf is an output from the fuel efficiency module, x is an outputfrom the neural network of the fuel efficiency module, and G is anoutput gain of the evolutionary-adaptation hierarchy. By lineartransformation of the output x of the neural network, the output yf fromthe fuel efficiency module does not become extremely high, i.e.,evolution progresses gradually as a whole. That is, an extreme change inengine behavior due to the evaluation or evolution is prevented.

After determining output yf of the fuel efficiency module for individualAn(1), the output from the evolutionary-adaptation hierarchy (tentativecompensation Yn(1)) is calculated by using a weighted mean of the outputyf and an output ya of the acceleration module whose couplingcoefficient is fixed (step 6). The summation in the weighted mean isdetermined by acceleration-weighting ratio α determined in theevaluation system. The tentative compensation Yn is expressed byequation (2).

    Yn=αya+(1-α)yf                                 (2)

wherein yf is the output from the fuel efficiency module, and ya is theoutput from the acceleration module. That is, when theacceleration-weighting ratio is 1, the tentative compensation Yn is anoutput only from the acceleration module. When theacceleration-weighting ratio is 0, the tentative compensation Yn is anoutput only from the fuel efficiency module.

After determining output Yn(1) of the evolutionary-adaptation hierarchyfor individual An(1), this tentative compensation Yn(1) is actuallyoutput from the evolutionary-adaptation hierarchy, and is added to thecontrol base value from the reflection hierarchy. The engine is operatedby the output compensated for by the tentative compensation Yn(1) (step7).

The evaluation system in the evolutionary-adaptation hierarchy receivesfeedback information related to fuel efficiency from the engine operatedby the output compensated for by the tentative compensation Yn(1)obtained from individual An(1), followed by calculating fuel efficiency(step 8). Based on the result, individual An(1) is evaluated bydetermining adaptability of individual An(1) (step 9). In the above,fuel efficiency is calculated from the travel distance and the amount ofconsumed fuel.

The above steps 4 through 9 continue until determination of adaptabilityof each of nine individuals, individuals An(1) through An(9) created instep 1, is completed. After completion of the above determination ofadaptability, the next process is activated (step 10). In the above, inorder to evaluate the adaptability of each individual, the processesfrom steps 4 through 9 from individual An(2) are not conducted beforechecking driving conditions (step 2), and only when the drivingconditions are the same as those for initial individual An(1) (step 3),is step 4 activated.

After determination of adaptability of all of the individuals iscompleted, it is determined whether or not the generation to which theindividuals belong is the final generation (step 11). If it is not thefinal generation, parent individuals are selected (step 12). In thisselection, a roulette-type selection method is employed, i.e., theparent individuals are stochastically selected based on the probabilitycorrelated to the adaptability of each individual.

In the above, if the alternation of generations is strictly performed,there is the possibility that individuals highly evaluated aredestroyed. To prevent destruction of all individuals belonging to theprevious generation, an elite reserve strategy is also employed, i.e.,an elite (highly evaluated individual) remains alive unconditionally. Inaddition, to maintain the ratio of the maximum adaptability to theaverage adaptability in a group consisting of multiple individuals, theadaptability is linearly transformed.

After selecting parent individuals, cross-over is performed using theselected individuals as parent individuals to create a second generationcomposed of nine children (step 13). The cross-over between individualsmay be single-point cross-over, double-point cross-over, or normaldistribution cross-over.

The normal distribution cross-over is a method of creating childrenbased on a rotation-symmetrical normal distribution with respect to anaxis connecting the parents, using chromosomes expressed by the actualnumber (individuals). The standard deviation of the normal distributionare correlated with the distance between the parents in terms of thecomponents in the direction of the main axis connecting the parents.Other components of the axis is made correlated with the distancebetween the line connecting the parents and a third parent sampled fromthe group. This cross-over method has an advantage that thecharacteristics of the parents are easily passed on to their children.

In addition, mutation of genes is caused in the created nine children byrandomly changing the gene value (the degree of coupling) at a givenprobability.

By the above processes, after the second generation is created, thecoupling coefficient of the neural network of the fuel efficiency moduleis fixed to that of an individual (elite), evolutionary treatment forthe acceleration module starts. After the evolutionary treatment of thefirst generation of the acceleration module is completed, the processesare repeated from step 1, evaluation of each individual of the secondgeneration, and selection are conducted (see FIG. 14). In the above, instep 1, it is determined whether or not there are encoded individuals,i.e., whether or not the generation is the second or more. If there areencoded individuals, no coding is conducted in step 1, and step 2 isactivated.

This processes are repeated until the generation reaches thepredetermined final generation. Accordingly, children composing eachgeneration evolve in accordance with the evaluation in the evaluationsystem, i.e., the user's preference. It is determined in step I1 whetheror not the generation is final. If it is determined in step 11 that thegeneration is final, an individual hiving highest adaptability (mostadaptable individual), i.e., an elite, is selected from the ninechildren (step 14). The coupling coefficient of the neural network ofthe fuel efficiency module is fixed at the gene possessed by the mostadaptable individual (step 15). The evolution of the fuel efficiencymodule ends.

In the acceleration module, the same treatment as in the fuel efficiencymodule is conducted until the generation reaches the final generation.In the above, evaluation in steps 8 and 9 for the acceleration module isconducted using an acceleration evaluation index. The accelerationevaluation index is calculated by dividing the acceleration by thechanging rate of the throttle opening. FIG. 13 shows the relationshipbetween a change in the speed of the vehicle and an accelerationevaluation index when the degree of the throttle opening is constant.

In addition, in the above-described genetic algorithm, the followingtechniques (1)-(3) are also considered:

(1) Mutation of Overlapped Individual

Despite the fact that different individuals are selected as parents forcross-over, if they are genetically identical, mutation is caused toboth parents at a higher probability than usual. In the above mutation,a change based on normal distribution is added to the selected gene.

(2) Avoidance of Cross-over of Same Individual

There is a possibility that one parent selected for cross-over is thesame individual as the other parent. If no action to stop the occurrenceis taken, diversity of the group will be lost. Thus, if the parents forcross-over are the same individual, one of the parents is replaced withanother individual, thereby avoiding the above occurrence.

(3) Regeneration

Instead of cross-over, a regeneration technique, which replaces all theindividuals in the group with other individuals at once.

By using the generic algorithm, evolution of the fuel efficiency moduleand the acceleration module is completed, and the neural network of eachmodule is fixed to the coupling coefficient of the most adaptableindividual. Accordingly, an evolutionary compensation Y is output fromthe evolutionary-adaptation hierarchy where the fuel efficiency moduleand the acceleration module are fixed.

This evolutionary-adaptation hierarchy Y is obtained by determining theoutput of the neural network of each module based on the input signal(the engine speed and the degree of the throttle opening), linearlytransforming the output of each neural network using the aforesaidequation (1) to obtain outputs yf and ya from the respective modules,and calculating the weighted mean of the outputs yf and ya, using theaforesaid equation (2).

As described above, by creating multiple individuals in each module andconducting cross-over of them, competition of multiple individualscauses the module to evolve to be a better module (competition betweenmodules of the same type). Further, by causing separately andalternately the fuel efficiency module and the acceleration module toevolve, the module which is changing can evolve to be adaptable inaccordance with the output from the module which is not changing,thereby performing cooperation between the different modules. In theabove, if summation of one of the control modules is small, thecharacteristics of the module are not readily expressed even if agenetic change is exerted on the control module. Thus, modules may bedesigned in such a way that only modules having a relatively largesummation may receive a genetic change.

Learning Hierarchy Adapted to Engine Control

The learning hierarchy is comprised of two neural networks A and B,wherein while one of them is functioning for learning, the other isfunctioning for operation.

The learning hierarchy learns the relationship between the input andoutput of the evolutionary-adaptation hierarchy, in combination with therelationship between the input and output of the neural networkfunctioning as a learning hierarchy for learning, after evolution ofeach module is completed in the evolutionary-adaptation hierarchy,thereby fixing the neural networks of the fuel efficiency module and theacceleration module at a degree of coupling of the most adaptableindividual. Meanwhile, the output from the evolutionary-adaptationhierarchy does not change with time and is output by the fuel efficiencymodule and the acceleration module which caused the previous evaluationequation to be maximum.

The aforesaid learning, the input and output of theevolutionary-adaptation hierarchy, and the input and output of theneural network for learning of the learning hierarchy are averaged at agiven step width to use data of the input and output to renew a set ofeducator data. For example, if the average engine speed per second is5,000 r.p.m.'s, and the average degree of the throttle opening is 20,the sum of these values and a fuel-injection compensation output fromthe evolutionary-adaptation hierarchy, and the neural network foroperation of the learning hierarchy (i.e., an evolutionary compensationand a basic compensation), is used as input and output data (see FIG.15). The thus-obtained input and output data are added to the previouseducator data to obtain new educator data. In the above, old educatordata, whose Euclidean distance from the new data in a set of educatordata is less than a given value, are deleted. This process is shown inFIG. 16. The initial values of a set of educator data are set so as tooutput zero for all input data.

The learning hierarchy learns a coupling coefficient of the neuralnetwork for learning based on the renewed set of educator data. Thelearning continues until a deviation between (a) a presumed controloutput, which is obtained from an output from the neural network forlearning (i.e., presumed compensation) and a control base value from thereflection hierarchy, and (b) the actual control output, is less than athreshold. After completing the learning, the neural network forlearning is switched to that for operation, while the neural networkpreviously for operation is switched to that for learning. After thisprocess, the learning hierarchy determines the basic compensation usingthe newly-obtained neural network for operation, and actually outputsthe result. When the learning hierarchy functions as above, the outputfrom the evolutionary-adaptation hierarchy is zero, i.e., control isconducted by the learning hierarchy and the reflection hierarchy.

The initial value of the neural network for operation in the learninghierarchy is set so as to output zero. Accordingly, in an initial state,control can be conducted only by the reflection hierarchy and theevolutionary-adaptation hierarchy.

The coupling coefficient of the neural network for operation which hascompleted learning can readably be saved in external memory means suchas a floppy disk and an IC card.

Overall Features and Other Features

As described above, after the evolution in the evolutionary-adaptationhierarchy is completed, and the learning hierarchy learns the evolution,the evolutionary-adaptation hierarchy is activated at given intervals asshown in FIG. 2 to check the presence of drift in the controlparticulars of the learning hierarchy. If there is drift, the fuelcontrol module and the acceleration control module start evolutionagain. When the user uses an external memory means storing the couplingcoefficient to activate the learning hierarchy using the couplingcoefficient pulled out of the external memory means, checking drift inthe control particulars of the evolutionary-adaptation hierarchy neednot be conducted, and the output from the evolutionary-adaptationhierarchy may be fixed to zero, i.e., the evolutionary-adaptationhierarchy is inactivated. Upon instructions from the user to start, theevolutionary-adaptation hierarchy can be activated.

By evaluating the user's preference in the evolutionary-adaptationhierarchy and accordingly evolving the fuel control module and theacceleration control module, the engine 1 is "trained" to suit theuser's preference, e.g., a fuel efficiency-weighted type or adrivability-weighted type. Further, by activating theevolutionary-adaptation hierarchy at given intervals, a course oftraining can be changed in accordance with a change in the user'spreference or a change in the engine or the vehicle itself with age.

The advantages in causing the evolutionary-adaptation hierarchy toevolve while limiting compensations and in causing the learninghierarchy to learn the evolution, are the following:

a. Diversity of the control modules in the evolutionary-adaptationhierarchy remains, and a wide range reference which is the feature ofthe genetic algorithm can be performed.

b. High-speed and more intellectual information processing in thelearning hierarchy can be obtained from trial-and-error-type informationprocessing in the evolutionary-adaptation hierarchy. Although thisfeature is not very significant in the engine control described above,the feature is very advantageous in route control of locomotive robots.

In the aforesaid example, two control modules are made for accelerationand fuel-efficiency, respectively. However, control modules can be madefor fuel-injection quantity, the ignition timing, or the like. In theabove, when the length of an intake pipe can be controlled, for example,the existing control modules need not be changed, but simply by addingan intake pipe length control module, integrated control can beperformed. In addition to the above, control output for controlling anengine can be the degree of the electric throttle opening, the timing ofactivation of intake and exhaust valves, the degree of valve lift, thetiming of activation of intake and exhaust control valves, or the like(see FIG. 3). In the above, the intake control valve is a valve providedin an intake pipe in order to control a tumbler and swirl. The exhaustcontrol valve is a valve provided in an exhaust pipe in order to controlexhaust pulsation.

In the aforesaid example, in the evolutionary process of the geneticalgorithm, the fuel efficiency module and the acceleration module evolvealternately, wherein one generation evolves at a time. However, theevolutionary process is not limited to the above. For example, thefollowing process is possible: First, the acceleration module is fixed,and the fuel efficiency module evolves until it reaches the finalgeneration to obtain the most adaptable control module, thereby fixingthe fuel efficiency module to the most adaptable control module, andthen the acceleration module starts evolving.

Further, the modules used in the aforesaid example are divided into two,the fuel efficiency module and the acceleration module, and each moduleis subjected to evolutionary treatment using genetic algorithm. However,the number of the modules used is not limited to two, and it can be oneor three or more.

In addition, in the genetic algorithm in the aforesaid example,evolution is completed when reaching the predetermined final generation.However, the timing of completion of evolution is not limited to theabove, and for example, evolution can be completed when the degree ofevolution of evolving individuals exceeds a predetermined evaluationrange.

In the aforesaid example, the driving state index P for presuming thedriver's preference and driving conditions is presumed using the neuralnetwork based on the distribution pattern of the gear position and themaximum r.p.m. However, the method of presuming the driving state indexP is not limited to the above, and any given method can be employed,such as fuzzy presumption. When presuming the driving state index P, anIF-THEN rule is described, and fuzzy presumption is performed (see FIG.17). The IF-THEN rule is, for example, "if the maximum engine speed ishigh in the first gear and in the second gear, the driving state index Pis extremely high."

In the neural network, the coupling coefficient is determined bylearning from the educator data, i.e., a black box approach. When fuzzypresumption is employed, an approach based on knowledge at the time ofdesigning the product is feasible.

The user's preference is determined by a distribution pattern of thegear position and the maximum engine speed in the aforesaid example.However, parameters to determine the user's preference are not limitedto the above, and any given parameters can be used. For example, byinstalling a detecting device for detecting a user's physiologicalindex, such as heart beat, blood pressure, body temperature, and brainwaves, in a helmet, gloves, or boots in the case of a motor bike, theuser's preference can be determined. Further, these physiologicalindices can be used for evaluating the user's state (driving state ofthe driver).

In the aforesaid example, upon evaluating the user's preference,training is conducted in accordance with the preference. Parameters todetermine a course of training are not limited to the above, and forexample, upon evaluating the user's skill, training can be conducted inaccordance with the skill. In the above, parameters to evaluate theskill are the degree of tilt of the vehicle, acceleration in thevertical direction of the vehicle, the use of the brake, the operatingratio of the front brake to the rear brake, and the like.

Further, in the aforesaid example, the leaning hierarchy is comprised ofa hierarchical neural network. However, the basic structure of thecontrol system of the learning hierarchy is not limited to the above,and for example, CMAC (Cerebellar Model Arithmetic Computer) can beused. CMAC is excellent in terms of additional learning and a high speedof learning, as compared with the hierarchical neural network.

Various Aspects of the Invention

As described above, the present invention includes various aspects asfollows:

1) An integrated control system, comprising steps of: judgingcharacteristics of a user and using conditions; and changingcharacteristics of a control system controlling a subject to becontrolled depending on said characteristics of the user and usingconditions based on a judged result.

2) In 1), said characteristics of said user and circumstances of use ispresumed by using a neural circuit network or fuzzy rule.

3) In 1) or 2), said characteristics of said user and circumstances ofuse is at least one of a user's preferences, skill and condition.

4) In 1), 2) or 3), said control characteristics can be adaptivelychanged in accordance with changes in using environment and/or age-baseddeterioration of a subject to be controlled.

5) In 1), 2), 3) or 4), a framework of a control system is comprised ofa three-hierarchial structure such as a reflection hierarchy as a bottomhierarchy, a learning hierarchy as a middle hierarchy andevolutionary-adaptation hierarchy as a top hierarchy.

6) In 5), a basic amount of said control output is output from saidreflection hierarchy, while outputs from said learning andevolutionary-adaptation hierarchy are compensatory amount relative tosaid basic amount.

7) In 5) or 6), said evolutionary-adaptation hierarchy includes at leastone control module which behaves autonomically, and said control systemcan be adaptively modified by competition and cooperation of saidcontrol module.

8) In 7), there are plural control modules, and said control system canbe adaptively modified by competition and cooperation of said pluralcontrol modules.

9) In 8), output ratio of plural control modules of saidevolutionary-adaptation hierarchy is varied depending oncharacteristic(s) of a user and circumstances of use.

10) In 7), 8) or 9), said accord and competition are performed bygenetic algorithm or multiagent systems.

11) In 10, when according/competing said control modules of saidevolutionary-adaptation hierarchy by using said genetic algorithm, it isarranged that an output of one of plural individuals produced from saidcontrol modules is constantly set to have zero value, while an initialstate of other individuals is determined randomly within a predeterminedrange, and during an evolutionary process by crossing of saidindividuals, a performance of each individual is prevented from beinglessened than a performance before the evolution.

12) In 10) or 11), a performance function in the genetic algorithm canbe automatically changed depending on characteristics of a user and/orcircumstances of the use.

13) In 12), a relationship between said performance function andcharacteristics of a user and/or circumstances of the use can be changedby a user's instruction.

14) In 5) through 13), an output gain of said evolutionary-adaptationhierarchy is arranged to be limited.

15) In 5) through 14), said learning hierarchy consists of two differentneural networks, such as operation and learning neural circuit networks.

16) In 15), a control characteristic obtained by the evolution of acontrol module in said evolutionary-adaptation hierarchy is learned in alearning neural circuit network in said learning hierarchy.

17) In 16), said learning hierarchy has a set of educator data forlearning, and a sum of outputs in said operational neural network forboth evolutionary-adaptation and learning hierarchy is output. Only thesum output during a certain period of time in the past is used forrenewing the neural circuit network in said learning hierarchy as neweducator data, while educator data in other regions is regarded as olddata.

18) In 15), 16) or 17), after said learning neural circuit network iscompleted, said learning neural circuit network functions as theexecution network, and said neural circuit network previouslyfunctioning as the execution network comes to function as the learningneural circuit network.

19) In any one of 15)-18), information regarding the learned neuralcircuit network in said learning hierarchy is recorded in an externalmemory device such as a floppy disc or IC card so as to store andretrieve the memory.

20) In any one of 5) through 19), said reflection hierarchy is arrangedto perform by using either one of a formula model, fuzzy rule, neuralcircuit network, map, or subsumption architecture.

21) In any one of 3) through 20), a user's skill is presumed based onsaid external conditions, and the function of the power source ischanged depending on the presumed skill.

22) In any one of 2) through 20), a user's condition is presumed byusing a physiological index, and performance of the power source ischanged based on the presumed user's conditions.

23) In 22), said physiological index refers to either one or more ofpulse, blood pressure, body temperature and an electro-encephalogram.

24) In any one of 1) through 23), said subject to be controlled isarranged to actively function.

25) In 24), said subject to be controlled is comprised of an engine.

26) In 25), said engine is an automobile engine, a user'scharacteristics are analyzed by his/her preferences, technical skill,and/or physical and mental states, and operational characteristics ofthe engine are modified based on the analysis.

27) In 26), a detection means detecting an operational state of avehicle is further included, wherein an operational state index, whichmatches at least either one of or both of (a) user's preferences andskill and (b) operational state and driving conditions of the engine, ispresumed based on at least part of the detection results. Operationalcharacteristics of the engine are modified based on said operationalstate index.

28) In 27), said operational state index is presumed by using either oneof or both of said neural circuit network and fuzzy rule.

29) In 27) or 28), it is further characterized in that the performancefunction in said genetic algorithm, in which said control module in saidevolutionary-adaptation hierarchy performs competition and cooperation,is modified based on said operational state index.

30) In 29), a relationship between said performance function andoperational state index is modified based on time to reach a maximumengine speed at each gear position, rate of change of an engine speedand input by an operator using an instruction input button.

31) In 27), 28), 29) or 30), said detection means detecting theoperational state of said vehicle is a detection means detecting enginespeed and gear positions.

32) In any one of 25) through 31), it is further characterized in that afuel injection volume, ignition timing, electronic throttle openingangles, intake/exhaust valve timings, valve lift amount, intake/exhaustcontrol valve timings are utilized for the control output for modifyingthe operational characteristics of the engine.

33) In any one of 26) through 32), it is further characterized in that auser's skill is presumed based on at least one of the following factors,such as the user's clutch operation speed, tilt of a vehicle, anacceleration degree of a vehicle in vertical directions, brake usage andratio of usage of front/rear brakes.

34) In any one of 26) through 33), it is further characterized in thatat least one means to detect an operator's physiological state ismounted in accessories worn by the user, and the user's condition ispresumed based on a detected physiological state.

35) In 34), said accessories refer to either one or more of helmet,gloves and boots.

36) In 24), said subject to be controlled is an auxiliary power of abicycle or wheel chair driven by an electric motor or engine, and acontrol characteristic of the control system refers to an assistingcharacteristic of said auxiliary power.

37) In 24), said subject to be controlled is a robot, and a controlcharacteristic of the control system refers to operationalcharacteristics of said robot.

38) In 37), said operational characteristics refers to at least one of apath selection, the manner of arm movement, moving speed or the mannerof speaking of said robot.

39) In 37) or 38), said robot is a personal robot.

40) In any one of 1) through 23), said subject to be controlled isarranged to passively operate.

41) In 40), said subject to be controlled is a steering system of avehicle, and said characteristic of said control system refers to asteering control characteristic of said steering system.

42) In 40), said subject to be controlled is a suspension system or seatof a vehicle, and said characteristic of said control system refers to adamper characteristic of said suspension system or seat.

43) In 41) or 42), a user's characteristics are analyzed by his/herpreferences, technical skill, and/or physical and mental states, andoperational characteristics are analyzed by the driving state of thevehicle, and control characteristics of the control system are modifiedbased on the analysis.

44) In 43), the system further includes a detection means detecting anoperational state of a vehicle, wherein an operational state indexmatching at least either one of or both of a user's preferences andskill, and/or operational state and driving condition of the engine ispresumed, at least partially based on the detection results, and theoperational characteristics of the engine are modified based on saidoperational state index.

45) In 44), said operational state index is presumed by using either oneof or both of said neural circuit network and fuzzy rule.

46) In 44) or 45), the system is further characterized in that theperformance function in said genetic algorithm, in which said controlmodule in said evolutionary-adaptation hierarchy performs competitionand cooperation, is modified based on said operational state index.

47) In any one of 43)-46), the system is further characterized in that auser's skill is presumed based on at least one of the following factors,such as the operator's clutch operation speed, tilt of a vehicle, thedegree of vertical acceleration of a vehicle, brake usage and ratio ofusage of front/rear brakes.

48) In any one of 43)-46), the system is further characterized in thatat least one means to detect a user's physiological state is/are mountedin accessories worn by the user, and the user's physical or mental stateis presumed based on a detected physiological state.

49) In 48), said accessories refer to either one or more of a helmet,gloves and boots.

It will be understood by those of skill in the art that numerousvariations and modifications can be made without departing from thespirit of the present invention. Therefore, it should be clearlyunderstood that the forms of the present invention are illustrative onlyand are not intended to limit the scope of the present invention.

What is claimed is:
 1. A method of real-time controlling an operationsignal outputted from a control unit to operate a machine, output ofwhich machine is manipulated by a user, said control unit beingprogrammed to formulate multiple control modules under predeterminedrules, each control module outputting an operation signal when receivinga pre-selected signal including a signal from the user, said methodcomprising the steps of:(a) formulating multiple control modules; (b)inputting to the multiple control modules a pre-selected signalincluding a signal from the user to output from the control module anoperation signal into the machine; (c) manipulating output of themachine by the user; (d) while the machine is in operation, selecting atleast one control module from the multiple control modules, which isadaptive to the user's manipulation pattern; (e) formulating subsequentmultiple control modules based on the selected control module(s), if theselected control module is not final; (f) repeating steps (b) through(e) at predetermined times, wherein the machine is operated adaptivelyto the user on a real-time basis, using the finally selected controlmodule.
 2. The method according to claim 1, wherein the input-outputrelationship of each control module is regulated by parameters, and instep (e), the multiple control modules are formulated by evolutionarycomputing wherein the parameters are used as genes.
 3. The methodaccording to claim 1, wherein steps (a) through (f) are repeatedperiodically while the machine is in operation.
 4. The method accordingto claim 1, wherein steps (a) through (f) are repeated on the user'srequest.
 5. The method according to claim 1, wherein information on thefinally selected control module is saved in a memory and retrieved onthe user's request.
 6. The method according to claim 1, wherein theuser's manipulation is for controlling output of the machine, and steps(a) through (f) are performed without the user's knowledge.
 7. Themethod according to claim 1, wherein the user's manipulation pattern isevaluated by fuzzy rules.